{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "bbbf3607",
   "metadata": {},
   "source": [
    "# 丢弃法Dropout\n",
    "\n",
    "视频：https://www.bilibili.com/video/BV1Y5411c7aY?p=1\n",
    "\n",
    "4.6章：https://zh-v2.d2l.ai/chapter_multilayer-perceptrons/dropout.html\n",
    "\n",
    "\n",
    "·除了控制权重向量的维数和大小之外，dropout也是避免过拟合的另一种工具。它们通常是联合使用的。\n",
    "\n",
    "·dropout常作用在多层感知机的隐藏层输出上，丢弃概率是控制模型复杂度的超参数\n",
    "\n",
    "·dropout将激活值 h 替换为具有期望值 h 的随机变量。\n",
    "\n",
    "·dropout仅在训练期间使用。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4ef7870",
   "metadata": {},
   "source": [
    "# 一、笔记"
   ]
  },
  {
   "attachments": {
    "image-2.png": {
     "image/png": 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"
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"
    }
   },
   "cell_type": "markdown",
   "id": "873572f9",
   "metadata": {},
   "source": [
    "## 1.训练中使用丢弃法\n",
    "\n",
    ">·通常将丢弃法用在**隐藏全连接层**的输出上  \n",
    ">将dropout应用到隐藏层，以p的概率将隐藏单元置为零\n",
    ">![image.png](attachment:image.png) ![image-2.png](attachment:image-2.png)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c16aae13",
   "metadata": {},
   "source": [
    "# 2.推理中的丢弃法\n",
    "\n",
    ">正则项只在训练中使用：他们影响模型参数的更新  \n",
    ">在推理过程中，丢弃法直接返回输出（无事发生）"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aebb206f",
   "metadata": {},
   "source": [
    "---\n",
    "---\n",
    "---\n",
    "# 二、代码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c9fe9dc5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch import nn\n",
    "from d2l import torch as d2l\n",
    "\n",
    "import torchvision\n",
    "from torch.utils import data\n",
    "from torchvision import transforms"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77978da9",
   "metadata": {},
   "source": [
    "## 1.导入数据集 fashion_mnist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5e698382",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\torchvision\\datasets\\mnist.py:498: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  ..\\torch\\csrc\\utils\\tensor_numpy.cpp:180.)\n",
      "  return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)\n"
     ]
    }
   ],
   "source": [
    "# 数据集\n",
    "\n",
    "batch_size = 256\n",
    "\n",
    "# 用多个进程读数据\n",
    "def get_dataloader_workers():  #@save\n",
    "    \"\"\"使用4个进程来读取数据。\"\"\"\n",
    "    return 4\n",
    "\n",
    "\n",
    "# 加载数据函数\n",
    "def load_data_fashion_mnist(batch_size, resize=None):  #@save\n",
    "    \"\"\"下载Fashion-MNIST数据集，然后将其加载到内存中。\"\"\"\n",
    "    trans = [transforms.ToTensor()]\n",
    "    if resize:\n",
    "        trans.insert(0, transforms.Resize(resize))\n",
    "    # 数据格式转换    \n",
    "    trans = transforms.Compose(trans)\n",
    "    # 读取数据集\n",
    "    mnist_train = torchvision.datasets.FashionMNIST(root=\"../data\", train=True, transform=trans, download=True)\n",
    "    mnist_test = torchvision.datasets.FashionMNIST(root=\"../data\", train=False, transform=trans, download=True)\n",
    "    # 加载数据集\n",
    "    return (data.DataLoader(mnist_train, batch_size, shuffle=True,\n",
    "                            num_workers=get_dataloader_workers()),\n",
    "            data.DataLoader(mnist_test, batch_size, shuffle=False,\n",
    "                            num_workers=get_dataloader_workers()))\n",
    "\n",
    "\n",
    "train_iter, test_iter = load_data_fashion_mnist(batch_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d70d716c",
   "metadata": {},
   "source": [
    "## 2.创建神经网络"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c4e96bb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 设置丢弃dropout概率\n",
    "dropout1, dropout2 = 0.2, 0.5\n",
    "\n",
    "# 构建神经网络\n",
    "net = nn.Sequential(\n",
    "        nn.Flatten(),         # 数据展平为1维\n",
    "        nn.Linear(784, 256),  # 全连接层 维度变换 （28*28）->（256）\n",
    "        nn.ReLU(),            # 激活函数Relu\n",
    "        # 在第一个全连接层之后添加一个dropout层\n",
    "        nn.Dropout(dropout1),\n",
    "        nn.Linear(256, 256),\n",
    "        nn.ReLU(),\n",
    "        # 在第二个全连接层之后添加一个dropout层\n",
    "        nn.Dropout(dropout2),\n",
    "        nn.Linear(256, 10))\n",
    "\n",
    "def init_weights(m):\n",
    "    if type(m) == nn.Linear:\n",
    "        nn.init.normal_(m.weight, std=0.01)\n",
    "\n",
    "net.apply(init_weights);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5cc943a",
   "metadata": {},
   "source": [
    "## 3.训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e196f18c",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "<Figure size 252x180 with 1 Axes>"
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     },
     "metadata": {
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     "output_type": "display_data"
    }
   ],
   "source": [
    "#定义训练次数num_epochs，精度lr，一次训练所选取的样本数batchsize\n",
    "num_epochs, lr, batch_size = 10, 0.5, 256\n",
    "# 定义损失函数\n",
    "loss = nn.CrossEntropyLoss()\n",
    "\n",
    "\n",
    "\n",
    "trainer = torch.optim.SGD(net.parameters(), lr=lr)\n",
    "d2l.train_ch3(net, train_iter, test_iter, loss, num_epochs, trainer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa8bc965",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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